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Infarstructure debt for institutional investors


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Infarstructure debt for institutional investors

  1. 1. Infrastructure debt for institutional investors Who is afraid of construction risk? Frédéric Blanc-Brude, Research Director EDHEC Risk Institute-AsiaNATIXIS/EDHEC Research Chair on Infrastructure Debt
  2. 2. Agenda• The quandary: financing infrastructure construction risk• The nature of infrastructure debt• Determinants of credit spreads• Systematic drivers of credit risk• Correlations and portfolio construction• Conclusions 2
  3. 3. The quandary: Who is afraid of construction risk?• Growing interest of institutional investors for long-term infrastructure investment – LDI & avoidance of market volatility• Growing political pressure to involve institutional money into the financing of new infrastructure investments• The difference boils down (in part) to the question of "construction risk" i.e. who should bear the risk of building new infrastructure? 3
  4. 4. 1The nature of infrastructure debt
  5. 5. The nature of infrastructure debt• The infrastructure debt universe• Project finance debt represents the majority of this universe → Relevant subset from an institutional investment point of view: unlisted, very large, 30-year track record, future origination• Project finance captures the characteristics of underlying infrastructure investments• Project finance benefits from a clear and internationally recognised definition since Basel-2 5
  6. 6. Infrastructure project financing volumes 6
  7. 7. Basel-2 definition"Project Finance (PF) is a method of funding in which investorslooks primarily to the revenues generated by a singleproject, both as the source of repayment and as security forthe exposure. In such transactions, investors are usually paidsolely or almost exclusively out of the money generated by thecontracts for the facilitys output, such as the electricity sold bya power plant. The borrower is usually an SPE that is notpermitted to perform any function other than developing,owning, and operating the installation. The consequence isthat repayment depends primarily on the projects cash Flowand on the collateral value of the project’s assets." (BIS, 2005) 7
  8. 8. Project finance SPE structure Source: Moody’s (2013) 8
  9. 9. The economics of project financing• Separate incorporation: self-selection of the project sponsors – Role of initial investment (construction phase) and project lifecycle• Leverage: project selection by the lenders – Non-recourse financing: an optimisation exercise – Role of lenders in SPE corporate governance – High leverage = low asset risk• Financial economics of the single-investment firm with high (initial) leverage and a long-term horizon – Impact of time vs. impact of de-leveraging• Project finance is different from standard corporate debt 9
  10. 10. Continuous de-leveragingand the single-project firm
  11. 11. 2The determinants of infrastructure debt credit spreads
  12. 12. Credit spread determinants• The immense majority of project finance debt is priced against a floating benchmark e.g. LIBOR• Three types of spread term structures: flat, down-trending and up-trending – Individual loans have different spreads at different points in time• Average loan spreads are a function of 3 types of factors – Loan characteristics – Macro-level factors – Project level factors• Systematic drivers of credit spreads exist in both cross-sectional (average) and longitudinal dimensions
  13. 13. Average loan spread determinants• Loan characteristics – Maturity – Size – Syndicate size• Macro-level factors – Country risks – Credit cycle – Business cycle• Project-level factors – Revenue risk models (determine business cycle impact) – Construction risk – Operating risks – Leverage
  14. 14. Average loan spread determinants• Existing studies pre-exist the 2007-9 financial crisis• New datasets: 1995 to 2012 – NATIXIS: 444 project loans – Thomson-Reuters: 1,962 project loans• Results of linear regressions confirm existing literature insights despite the impact of the crisis of average spreads – Project finance loans have lower spreads if they have longer maturities and a larger size – Revenue risk models are a significant driver of credit spreads – Construction risk is not (proxies suggest) – After 2008, the collapse of benchmark rates had a very significant positive impact on spreads
  15. 15. Panel regression results (coef. estimates)
  16. 16. Average credit spreads
  17. 17. Longitudinal spread determinants• Two sub-samples: down-trending and up- trending (according to the average difference of annual change in spread)• Spreads change in time to reflect change in risk profile (down) or to trigger a refinancing operation (a re-setting of risk pricing to match the change in risk profile)• Statistical results (panel regression with fixed effects) are very significant• We observe differential risk pricing during the lifecycle
  18. 18. Longitudinal spread determinants (panel regression fixed effects)
  19. 19. Generic spread profiles of infrastructure debt
  20. 20. 3Systematic drivers of credit risk in infrastructure debt
  21. 21. Return and risk measures• Once the determinants of credit spreads (yield to maturity) is known, the excepted return is a function of default and recovery rates and can be written: EARi = YTMi – ELi (Altman 1996) With the expected loss ELi = LGDi x PDi• Likewise, the unexpected loss is written ULi = LGDi x √(PDi x (1-PDi))
  22. 22. Credit risk studies for project debt• Majors data collection efforts by rating agencies have been on-going for more than ten years• 10-year cumulative probabilities of default are observed to be around 10%• Loss-given default (1-recovery) fluctuates between 25% and 0%. In more than two thirds of cases in the largest sample, recovery rate =100%• Credit risk dynamics make the marginal PDs more informative
  23. 23. Predictable credit risk migrations Source: Moody’s (2013)
  24. 24. Default intensity as a function of year-from-origination 0.025 0.025 Observed PD Observed PD Fitted PD Fitted PD 0.02 0.02 Prop. of DefaultsProp. of Defaults 0.015 0.015 0.01 Year 0 0.01 Year 1 0.005 0.005 0 0 0 5 10 15 20 0 5 10 15 20 Year Year 0.03 0.025 Observed PD Observed PD Fitted PD Fitted PD 0.025 0.02 0.02 Prop. of Defaults Prop. of Defaults 0.015 0.015 Year 2 0.01 Year 3 0.01 0.005 0.005 0 0 2 4 6 8 10 12 14 16 18 20 2 4 6 8 10 12 14 16 18 20 Year Year
  25. 25. Default intensity as a function of year-from-origination
  26. 26. Risk adjusted measure of infrastructure debt as a function of year-from- origination• The excepted return can now be written as a function of time from origination: EARit = YTMit – ELit With the expected loss ELit = LGDit x PDit• Likewise, the unexpected loss is written ULit = LGDit x √(PDit x (1-PDit))• Like credit spreads, both expected return and risk are a function of risk factors for the average instrument i over a lifecycle lifecycle defined by t =1,2,…T• This plays an instrumental role at the portfolio construction stage: the lifecycle becomes an important dimension of efficient infrastructure debt portfolios
  27. 27. 4Correlations & Portfolio Construction
  28. 28. Portfolio return & risk measures• Using the expected and unexpected losses already defined, we can write• The debt portfolio’s return measure: Rp = Σi=1N wi.EARit• The debt portfolio’s risk measure: ULp = Σi=1N Σj=1N wi.wj.ULit.ULjt.ρijt For debt instruments i and j at time from origination t
  29. 29. Default correlations• Existing research on default correlation in corporate debt boils down to two stylised facts – Default correlations are low in ‘normal’ times – Default correlations are a function of the business cycle• Casual observation of project finance default rates suggests that the business cycle plays an important role• But we know that year-from-origination and project-specific factors should also explain defaults at any given point in the business cycle… – We use panel regression to separate the effect of the business cycle from that of the project cycle on the covariance of default probabilities
  30. 30. Project finance PDs by calendar year (global sample) Source: Moody’s (2013)
  31. 31. Marginal PDs by calendar year vs. year of origination
  32. 32. Panel regression(calendar years fixed effect)
  33. 33. Default correlations of PDsbetween years of origination (significant 1%)
  34. 34. Portfolio construction• With these (partial) estimates of default correlations we can compute portfolio returns for a single period using the variable ‘year- from-origination’ to capture the effect of the lifecyle on expected returns and risk – The objective is to illustrate the diversification potential of investing across the infrastructure project lifecycle – We built to portfolios: • One invested across ten years of project lifecycle (including construction) • Another one invested only in post-construction/mature years (after year 5)
  35. 35. Efficient frontier with and without construction risk (illustration) 200 190Expected returns (basis points) 180 Including ‘construction risk’ 170 160 Post construction debt portfolio frontier 150 140 0.5 1 1.5 2 2.5 3 Risk (basis points) 141.15 Expected returns (basis points) 141.10 141.05 141.00 140.95 excluding ‘construction risk’ 140.90 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 Risk (basis points)
  36. 36. 5Conclusions
  37. 37. Infrastructure debt portfolio construction: remunerated & systematic risk factors• Theory and evidence suggest that within a large sample of project finance loans, several subsets can be identified that capture remunerated exposure to different systematic risk factors• Two subsets standout as prime candidates to improve portfolio diversification – Revenue risk models creating three subsets: full, partial and no commercial risk – The project lifecycle, which captures the evolution of the ‘single-investment firm’ from the investment, including construction, to the operating stage.
  38. 38. Infrastructure debt: the benefits ‘lifecycle diversification’• We have show that substantial diversification benefits can be created by investing in infrastructure project debt at different points in the infrastructure project lifecycle.• This conclusion is a direct consequence of: – The systematic change of risk profile of infrastructure project debt during its life – The matching change in spreads observed in project loans as they age – The differences in default correlations between different years from origination• If investing across the entire lifecycle of infrastructure projects improves diversification then investors should welcome ‘construction risk’ in their infrastructure debt portfolios
  39. 39. What construction risk anyway?• Recent research on construction risk confirms what theory suggests: on average, in project finance, construction risk is idiosyncratic (zero-mean = fully diversifiable) and is not as high as in public infrastructure projects. Construction cost overruns in public and private infrastructure projects 70 60 50 40 30 20 10 0 -80 -60 -40 -20 0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 Public construction risk - decision to build (Flyvbjerg dataset, n=110, 1950-2000) Project finance construction risk - financial close (NATIXIS dataset, n=75, 1993-2010) Blanc-Brude & Makovsek 2013
  40. 40. So who is afraid of construction risk?• Most existing infrastructure project finance debt prices the changes in project risk profiles – The average change in systematic credit risk is predictable, and systematic risk is remunerated – It is not only a feature of ‘legacy’ project debt. What matters is that with project finance, by design, credit risk can be priced over the lifecycle.• As a consequence, institutional investors need ‘construction risk’ to build efficient portfolios of infrastructure debt – As long as risk is priced across the lifecycle this conclusion holds• Conversely, for this conclusion to hold, risk should be priced across the lifecycle: this unique feature of project financing allows solving the initial quandary – Adequate pricing of systematic risk across the infrastructure project lifecycle can lead to both more efficient infrastructure debt portfolios and the financing of new infrastructure to support growth in Europe and
  41. 41. Selected references• Altman, E. (1996, October). Corporate Bond and Commercial Loan Portfolio Analysis. Centre for Financial Institutions Working Papers 96-41, Wharton School Centre for Financial Institutions, University of Pennsylvania.• Blanc-Brude, F. and D. Makovsek (2013, January). Construction risk in infrastructure project Finance, EDHEC Business School Working Papers• Blanc-Brude, F. and R. Strange (2007). How Banks Price Loans to Public-Private Partnerships: Evidence from the European Markets. Journal of Applied Corporate Finance 19(4), 94--106.• Moodys (2013, February). Default and recovery rates for project Finance bank loans1983-2011. Technical report, Moodys Investor Service, London, UK.
  42. 42. Who is afraid ofconstruction risk?byFrédéric Blanc-Brude*Omneia IsmailAvailable online in hard copy at this event*